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version 0.4

Sequential model-based optimization toolbox.

People: The scikit-optimize contributors


|Travis Status| |CircleCI Status|


Scikit-Optimize, or ``skopt``, is a simple and efficient library to
minimize (very) expensive and noisy black-box functions. It implements
several methods for sequential model-based optimization. ``skopt`` aims
to be accessible and easy to use in many contexts.

The library is built on top of NumPy, SciPy and Scikit-Learn.

We do not perform gradient-based optimization. For gradient-based
optimization algorithms look at
`here <>`_.

.. figure::
:alt: Approximated objective

Approximated objective function after 50 iterations of ``gp_minimize``.
Plot made using ``skopt.plots.plot_objective``.

Important links

- Static documentation - `Static
documentation <>`__
- Example notebooks - can be found in the
`examples directory <>`_.
- Issue tracker -
- Releases -


The latest released version of scikit-optimize is v0.4, which you can install

pip install numpy # install numpy explicitly first
pip install scikit-optimize

Getting started

Find the minimum of the noisy function ``f(x)`` over the range
``-2 < x < 2`` with ``skopt``:

.. code:: python

import numpy as np
from skopt import gp_minimize

def f(x):
return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) *
np.random.randn() * 0.1)

res = gp_minimize(f, [(-2.0, 2.0)])

For more control over the optimization loop you can use the ``skopt.Optimizer``

.. code:: python

from skopt import Optimizer

opt = Optimizer([(-2.0, 2.0)])

for i in range(20):
suggested = opt.ask()
y = f(suggested)
opt.tell(suggested, y)
print('iteration:', i, suggested, y)

Read our `introduction to bayesian
optimization <>`__
and the other
`examples <>`__.


The library is still experimental and under heavy development. Checkout
the `next
milestone <>`__
for the plans for the next release or look at some `easy
issues <>`__
to get started contributing.

The development version can be installed through:


git clone
cd scikit-optimize
pip install -e.

Run all tests by executing ``pytest`` in the top level directory.

To only run the subset of tests with low run time, you can use ``pytest -m 'fast_test'``. On the other hand ``pytest -m 'slow_test'`` is also possible. To exclude all slow running tests try ``pytest -m 'not slow_test'``.

This is implemented using pytest `attributes <>`__. If a tests runs longer than 1 second, it is marked as slow, else as fast.

All contributors are welcome!

.. |Travis Status| image::
.. |CircleCI Status| image::



You can download the latest distribution from PyPI here:

Using pip

You can install scikit-optimize for yourself from the terminal by running:

pip install --user scikit-optimize

If you want to install it for all users on your machine, do:

pip install scikit-optimize
On Linux, do sudo pip install scikit-optimize.

If you don't yet have the pip tool, you can get it following these instructions.

This package was discovered in PyPI.